Various embodiments of the present invention relate to prediction models, and more specifically, to a method and apparatus for updating a prediction model used for time series data.
With the development of technologies such as computer, data communication and real-time monitoring, time series data have been widely used in various respects such as device monitoring, production line management and financial analysis. Time series data refer to a set of measured values arranged in time order. For example, time series data may be stored in a database or in other manner.
Measured values may include various kinds of data. For example, in an application environment of monitoring users' access to an online banking system, measured values being collected may include users' access occurrences to the online banking system at various time points, time series data here may be stored as, for example, a sequence x1, x2, . . . , xi, . . . , xn, and data measured at the ith time point is a value xi. In an application environment of weather forecasts, measured values being collected may be multivariate, and may include, for example, temperature, humidity, pressure, wind force, etc. At this point, 4 groups of time series data will be obtained, and data measured at the ith time point may be temi, humi, prei, and windi respectively.
Typically value distribution of time series data follows a certain pattern, so future data changes may be predicted based on historical measured values that were collected in a past time period. For example, in the above example of monitoring users' access occurrences to an online banking system, various resource configurations in the online banking system may be adjusted accordingly based on predicted change of access occurrences, so as to be adapted to user access demands in different time periods and further increase resource utilization efficiency in the online banking system.
In the prior art, there have been developed technical solutions for building a prediction model based on historical measured values within a specific time period (e.g., training time window) and further for predicting values in a future given time period (e.g., prediction time window). However, prediction models often vary with the elapse of time. According to the existing technical solutions, it is impossible to ascertain whether an existing prediction model matches or does not match real measured values, so the prediction model has to be updated frequently with the elapse of time, which leads to a huge computation load. On the one hand, the prior art fails to provide a notification of when to update a prediction model and then generate a new prediction model; on the other hand, since after the updated new prediction model will depend on a selection of historical data, it becomes a focus of attention how to select an appropriate range of huge historical data for generating a new prediction model.